Genome-Wide Gene-Set Analysis Approaches in Amyotrophic Lateral Sclerosis
Abstract
:1. Introduction
2. Limitations on Single-Gene Analysis
3. A Brief Overview of Gene-Set Analysis
3.1. The Structure of Gene-Set Analysis
- Each SNP is assigned to a gene—using specific annotation files in order to map each SNP into a gene region based on a kilobase window around the gene so that the researcher can additionally include regulatory elements—and each gene is then tested for its association with the phenotype.
- Genes are mapped to gene sets, and an association measure is computed for each gene set.
3.2. Main Categories of Gene-Set Analysis Methods
- Mean-based, where the gene-set association measures are summarized using the mean or sum of the gene associations.
- Count-based, where the genes are labelled as “significant” or “not significant”, and only “significant” genes determined by a specific cut-off are considered in the gene-set test statistic.
- Rank-based, where the genes in the gene-level matrix are ranked by their association with the phenotype and then an overrepresentation of the genes that belong in the gene set and also are at the top of that ranking is computed.
3.3. Gene-Set Analysis Confounding
4. Recent Approaches in ALS Genome-Wide Gene-Set Analysis Studies
5. Discussion
5.1. Cohort Size Affects the Power of Genome-Wide GSA
5.2. Limitations on Dimensionality Reduction Approaches
5.3. Comparing the Collected Gene-Set Analysis Methods
5.4. Gene-Set Analysis Deepens Our Understanding of the Implicated ALS Functional Pathways
6. Conclusions
- The use of large cohort sizes can increase the power of genome-wide gene-set analyses;
- Comprehensive, transparent and reproducible genomic quality control strategies are likely to support more consistent biological findings;
- Data-driven and holistic approaches in the selection of genes and gene-set annotation databases are preferable;
- Selection of competitive GSA methods and mean-based statistics provide a better performance, and the biological assumptions are more consistent with a real-life complex functional network;
- Detailed and transparent GSA methodology can contribute to reproducible research results and informed decision-making;
- Enhanced visualisation approaches may aid interpretation, e.g., Enrichment Networks.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Studies | Input Data | Ancestry | GSA Software | GS Annotation |
---|---|---|---|---|
[41] | 27,205 cases and 110,881 controls [41,42,43] eQTL data | European, Japanese, Chinese | FUMA, MAGMA, Downstreamer | G0, HPO, REACTOME |
[44] | 20,806 cases and 59,804 controls [14] eQTL data dbGaP Ac. phs000424.v8.p2 | European | g:Profiler, Enrichr, GSEA | G0, KEGG |
[45] | 12,577 cases and 23,475 controls [20] 5605 cases and 24,110 controls [14] 2411 cases and 10,322 controls [14] | European | PRS approach | MSigDB |
[46] | 12,577 cases and 23,475 controls [20], eQTL data [47] | European | GSEA | KEGG |
[42] | 12,577 cases and 23,475 controls [20], 1234 cases and 2850 controls, 431 cases and 567 controls, [42] | European, Chinese, Australian | MAGMA | NA |
[48] | 276 ALS cases and 271 controls [49], 221 cases and 216 controls [50] | American, Irish | WebGestalt | GO |
[51] | 276 cases and 271 controls [49] | American | ICSNPathway | KEGG, BioCarta, GO BP, GO MF |
[52] | 276 cases and 271 controls [49], 221 cases and 211 controls [50] | American, Irish | WebGestalt | KEGG |
[53] | 250 cases and 250 controls | Chinese Han | WebGestalt | KEGG |
Software | Input Data | Null Hypothesis | GS Method | Studies |
---|---|---|---|---|
Downstreamer | p-values, eQTL | Competitive | Generalized least-squares regression | [41] |
Enrichr | Gene list | Competitive | Overrepresentation/hypergeometric test | [44] |
FUMA | eQTL & GWAS | Competitive | Overrepresentation | [41] |
g:Profiler | Gene list | Competitive | Overrepresentation/hypergeometric test | [44] |
GSEA/i-GSEA | p-values | Competitive | rank-based, (KS test) | [44,46,51] |
MAGMA | Genotypes, p-values | Competitive | Linear regression | [42] |
WebGestalt | Gene list | Competitive | Overrepresentation/hypergeometric test | [48,52,53] |
Studies | Multiple Testing Correction Method | Main Findings |
---|---|---|
[41] | Bonferroni | Cerebral cortical atrophy (p-value = 1.8 × 10−8), Abnormal nervous system electrophysiology (p-value = 4.1 × 10−7) Distal amyotrophy (p-value = 8.6 × 10−7), Membrane trafficking (p-value = 4.2 × 10−6), Intra-Golgi and retrograde Golgi-to-ER trafficking (p-value =1.4 × 10−5) Macroautophagy (p-value = 3.2 × 10−5) |
[44] | FDR < 0.05 | Vesicle-mediated transport in synapse (adjusted p-value = 7.58 × 10−7), Glutamatergic synapse (adjusted p-value = 4.20 × 10−6) Vesicle docking involved in exocytosis (adjusted p-value = 3.30 × 10−5) |
[45] | FDR < 0.05 | Neuron projection morphogenesis, Membrane trafficking, Signal transduction mediated by ribonucleotides |
[46] | Empirical p-values | Peroxisome (empirical p-value = 0.006), Citrate cycle TCA cycle (empirical p-value = 0.025), Tight Junction (p-value NA) PPAR signaling pathway (empirical p-value = 0.025), SNARE interactions in vesicular transport (empirical p-value = 0.027), Arachidonic acid metabolism (empirical p-value = 0.040), Glycolysis-gluconeogenesis (empirical p-value = 0.043) |
[42] | NA | No significant pathways were detected after multiple testing correction |
[48] | NA | Nervous system development (adjusted p-value = 1.13 × 10−9) |
[51] | FDR < 0.05 | Chromatin assembly (FDR = 0.001), Nucleosome assembly (FDR = 0.018) |
[52] | FDR < 0.05 | RNA transport (adjusted p-value = 1.00 × 10−3), Vascular smooth muscle contraction (adjusted p-value = 1.80 × 10−3), Neuroactive ligand-receptor interaction (adjusted p-value = 6.30 × 10−3), Systemic lupus erythematosus (adjusted p-value = 6.30 × 10−3), Chemokine signaling pathway (adjusted p-value = 6.30 × 10−3), Hematopoietic cell lineage (adjusted p-value = 6.30 × 10−3), Cytosolic DNA-sensing pathway (adjusted p-value = 1.30 × 10−2), Protein processing in ER (adjusted p-value = 1.62 × 10−2), Alzheimer’s disease (adjusted p-value = 1.69 × 10−2), Parkinson’s disease (adjusted p-value = 3.12 × 10−2), Oxidative phosphorylation (adjusted p-value = 3.26 × 10−2), Cytokine–cytokine receptor interaction (adjusted p-value = 3.37 × 10−2) |
[53] | FDR < 0.05 | Phosphatidylinositol signaling system (adjusted p-value = 0.0011), Pathways in cancer (adjusted p-value = 0.0011), Wnt signaling pathway (adjusted p-value = 0.0020), Axon guidance (adjusted p-value = 0.0021), MAPK signaling pathway (adjusted p-value = 0.0021), Neurotrophin signaling pathway (adjusted p-value = 0.0021), Arrhythmogenic right ventricular cardiomyopathy (adjusted p-value = 0.0044), Colorectal cancer (adjusted p-value = 0.0099), Arachidonic acid metabolism (adjusted p-value = 0.0454), T-cell receptor signaling pathway (adjusted p-value = 0.0488) |
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Vasilopoulou, C.; Duguez, S.; Duddy, W. Genome-Wide Gene-Set Analysis Approaches in Amyotrophic Lateral Sclerosis. J. Pers. Med. 2022, 12, 1932. https://doi.org/10.3390/jpm12111932
Vasilopoulou C, Duguez S, Duddy W. Genome-Wide Gene-Set Analysis Approaches in Amyotrophic Lateral Sclerosis. Journal of Personalized Medicine. 2022; 12(11):1932. https://doi.org/10.3390/jpm12111932
Chicago/Turabian StyleVasilopoulou, Christina, Stephanie Duguez, and William Duddy. 2022. "Genome-Wide Gene-Set Analysis Approaches in Amyotrophic Lateral Sclerosis" Journal of Personalized Medicine 12, no. 11: 1932. https://doi.org/10.3390/jpm12111932